Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory121.0 B

Variable types

Numeric11
Categorical3
Text1
Boolean1

Alerts

city is highly imbalanced (96.5%)Imbalance
homeType is highly imbalanced (87.2%)Imbalance
hasSpa is highly imbalanced (58.9%)Imbalance
lotSizeSqFt is highly skewed (γ1 = 64.99614816)Skewed
uid has unique valuesUnique
garageSpaces has 4458 (44.6%) zerosZeros
numOfPatioAndPorchFeatures has 6072 (60.7%) zerosZeros

Reproduction

Analysis started2024-02-28 02:36:25.557990
Analysis finished2024-02-28 02:36:55.648766
Duration30.09 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

uid
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7626.6219
Minimum1
Maximum15170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:36:55.988787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile789.95
Q13838.75
median7603.5
Q311435.75
95-th percentile14424.05
Maximum15170
Range15169
Interquartile range (IQR)7597

Descriptive statistics

Standard deviation4380.4869
Coefficient of variation (CV)0.57436791
Kurtosis-1.2027825
Mean7626.6219
Median Absolute Deviation (MAD)3804
Skewness-0.0068791886
Sum76266219
Variance19188665
MonotonicityNot monotonic
2024-02-27T21:36:56.289675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1748 1
 
< 0.1%
5686 1
 
< 0.1%
7166 1
 
< 0.1%
11522 1
 
< 0.1%
9608 1
 
< 0.1%
7326 1
 
< 0.1%
2 1
 
< 0.1%
3084 1
 
< 0.1%
1509 1
 
< 0.1%
3367 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
ValueCountFrequency (%)
15170 1
< 0.1%
15169 1
< 0.1%
15167 1
< 0.1%
15166 1
< 0.1%
15165 1
< 0.1%
15164 1
< 0.1%
15161 1
< 0.1%
15160 1
< 0.1%
15157 1
< 0.1%
15156 1
< 0.1%

city
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
austin
9898 
del valle
 
57
pflugerville
 
29
driftwood
 
8
manchaca
 
3
Other values (2)
 
5

Length

Max length16
Median length6
Mean length6.0423
Min length6

Characters and Unicode

Total characters60423
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaustin
2nd rowaustin
3rd rowaustin
4th rowaustin
5th rowaustin

Common Values

ValueCountFrequency (%)
austin 9898
99.0%
del valle 57
 
0.6%
pflugerville 29
 
0.3%
driftwood 8
 
0.1%
manchaca 3
 
< 0.1%
dripping springs 3
 
< 0.1%
west lake hills 2
 
< 0.1%

Length

2024-02-27T21:36:56.575476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-27T21:36:56.789700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
austin 9898
98.4%
del 57
 
0.6%
valle 57
 
0.6%
pflugerville 29
 
0.3%
driftwood 8
 
0.1%
manchaca 3
 
< 0.1%
dripping 3
 
< 0.1%
springs 3
 
< 0.1%
west 2
 
< 0.1%
lake 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 9966
16.5%
i 9946
16.5%
u 9927
16.4%
s 9908
16.4%
t 9908
16.4%
n 9907
16.4%
l 264
 
0.4%
e 176
 
0.3%
v 86
 
0.1%
d 76
 
0.1%
Other values (11) 259
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60359
99.9%
Space Separator 64
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9966
16.5%
i 9946
16.5%
u 9927
16.4%
s 9908
16.4%
t 9908
16.4%
n 9907
16.4%
l 264
 
0.4%
e 176
 
0.3%
v 86
 
0.1%
d 76
 
0.1%
Other values (10) 195
 
0.3%
Space Separator
ValueCountFrequency (%)
64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60359
99.9%
Common 64
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9966
16.5%
i 9946
16.5%
u 9927
16.4%
s 9908
16.4%
t 9908
16.4%
n 9907
16.4%
l 264
 
0.4%
e 176
 
0.3%
v 86
 
0.1%
d 76
 
0.1%
Other values (10) 195
 
0.3%
Common
ValueCountFrequency (%)
64
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9966
16.5%
i 9946
16.5%
u 9927
16.4%
s 9908
16.4%
t 9908
16.4%
n 9907
16.4%
l 264
 
0.4%
e 176
 
0.3%
v 86
 
0.1%
d 76
 
0.1%
Other values (11) 259
 
0.4%
Distinct9982
Distinct (%)99.8%
Missing1
Missing (%)< 0.1%
Memory size78.3 KiB
2024-02-27T21:36:57.201126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3988
Median length1784
Mean length441.24872
Min length1

Characters and Unicode

Total characters4412046
Distinct characters118
Distinct categories18 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9974 ?
Unique (%)99.7%

Sample

1st rowMULTIPLE OFFERS submit best & final to Agent by Mon 21st - 5pm. Appt with Agent. RARE PANORAMIC VIEW LOT IN JESTER ESTATES SEE FOR MILES!! Home sits on Cul-de-sac & backs to a Preserve. Stunning remodeled Kitchen & Bathrooms. Master suite is a private sanctuary with chic master bath, huge bedroom, walk-in closet & private deck. Jester has a pool, park, tennis courts & feeds into Anderson High. This home has been well loved & features 3 living areas, an office, & 3 car garage.
2nd row4644 Hoffman Dr, Austin, TX 78749 is a single family home that contains 2,059 sq ft and was built in 1997. It contains 4 bedrooms and 3 bathrooms.
3rd row6804 Canal St, Austin, TX 78741 is a single family home that contains 832 sq ft and was built in 1952. It contains 2 bedrooms and 1 bathroom.
4th rowBeautiful large lot with established trees. Lovely, light filled, spacious home, with large dining area, living room and kitchen in open floor plan layout. Large family-friendly back yard, newly redone patio that spans the back of the home with built-in fire pit in the yard and a newly built large shed. New laminate floors and tile throughout, renovated bathroom in 2017. Upgraded kitchen with stainless appliances, new washer/dryer. Built-in closet, laundry room and utility room. LED recessed lighting, low-profile ceiling fans throughout. Wired for Google Fiber. Neighborhood Description The Garrison Park Neighborhood Planning Area is located in southwest Austin. The boundaries for the planning area are Stassney on the north, the City of Sunset Valley on the west, William Cannon on the south and South First on the east.
5th rowStunning NW Hills designer remodel by Cedar and Oak Homes ~ Wonderful open & bright concept w/vaulted ceilings & formal dining room ~ Gorgeous kitchen features custom wood cabinetry, quartz countertops, double pantry, custom built-in breakfast nook and KitchenAid appliances ~ Beautiful oak engineered hardwood flooring throughout ~ Spacious Master bedroom with fantastic master bathroom ensuite ~ All new exterior Hardie Plank siding, Elevate Low-E windows, mechanical systems & fantastic new landscaping
ValueCountFrequency (%)
and 28193
 
4.0%
the 17917
 
2.5%
a 15747
 
2.2%
in 14682
 
2.1%
with 13482
 
1.9%
to 12086
 
1.7%
home 11518
 
1.6%
11109
 
1.6%
of 7985
 
1.1%
is 7404
 
1.1%
Other values (25489) 563513
80.1%
2024-02-27T21:36:58.004030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
706638
16.0%
e 350850
 
8.0%
a 284137
 
6.4%
o 281010
 
6.4%
t 271499
 
6.2%
n 246872
 
5.6%
i 241484
 
5.5%
r 217781
 
4.9%
s 210463
 
4.8%
l 157938
 
3.6%
Other values (108) 1443374
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3272320
74.2%
Space Separator 706815
 
16.0%
Uppercase Letter 178137
 
4.0%
Other Punctuation 138783
 
3.1%
Decimal Number 82473
 
1.9%
Control 14246
 
0.3%
Dash Punctuation 13686
 
0.3%
Math Symbol 1700
 
< 0.1%
Close Punctuation 1560
 
< 0.1%
Open Punctuation 1554
 
< 0.1%
Other values (8) 772
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 350850
10.7%
a 284137
 
8.7%
o 281010
 
8.6%
t 271499
 
8.3%
n 246872
 
7.5%
i 241484
 
7.4%
r 217781
 
6.7%
s 210463
 
6.4%
l 157938
 
4.8%
d 133407
 
4.1%
Other values (21) 876879
26.8%
Uppercase Letter
ValueCountFrequency (%)
T 16667
 
9.4%
A 16040
 
9.0%
S 14316
 
8.0%
C 12115
 
6.8%
E 10410
 
5.8%
L 8890
 
5.0%
R 8430
 
4.7%
B 8304
 
4.7%
I 8212
 
4.6%
O 8144
 
4.6%
Other values (18) 66609
37.4%
Other Punctuation
ValueCountFrequency (%)
, 53365
38.5%
. 48162
34.7%
! 10647
 
7.7%
/ 9465
 
6.8%
& 8370
 
6.0%
' 2941
 
2.1%
* 2509
 
1.8%
: 1293
 
0.9%
; 768
 
0.6%
" 695
 
0.5%
Other values (7) 568
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 13900
16.9%
1 12502
15.2%
0 10615
12.9%
3 9548
11.6%
7 8432
10.2%
4 6807
8.3%
5 6586
8.0%
8 6153
7.5%
9 4804
 
5.8%
6 3126
 
3.8%
Math Symbol
ValueCountFrequency (%)
+ 842
49.5%
~ 694
40.8%
< 58
 
3.4%
= 48
 
2.8%
| 28
 
1.6%
> 27
 
1.6%
± 3
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 13649
99.7%
28
 
0.2%
9
 
0.1%
Control
ValueCountFrequency (%)
7398
51.9%
6811
47.8%
37
 
0.3%
Other Number
ValueCountFrequency (%)
½ 23
88.5%
¼ 2
 
7.7%
² 1
 
3.8%
Space Separator
ValueCountFrequency (%)
706638
> 99.9%
  177
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1553
99.6%
] 7
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 1548
99.6%
[ 6
 
0.4%
Currency Symbol
ValueCountFrequency (%)
$ 557
98.1%
¢ 11
 
1.9%
Final Punctuation
ValueCountFrequency (%)
139
89.7%
16
 
10.3%
Modifier Symbol
ValueCountFrequency (%)
˜ 1
50.0%
` 1
50.0%
Initial Punctuation
ValueCountFrequency (%)
9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Format
ValueCountFrequency (%)
­ 2
100.0%
Other Symbol
ValueCountFrequency (%)
® 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3450457
78.2%
Common 961589
 
21.8%

Most frequent character per script

Common
ValueCountFrequency (%)
706638
73.5%
, 53365
 
5.5%
. 48162
 
5.0%
2 13900
 
1.4%
- 13649
 
1.4%
1 12502
 
1.3%
! 10647
 
1.1%
0 10615
 
1.1%
3 9548
 
1.0%
/ 9465
 
1.0%
Other values (49) 73098
 
7.6%
Latin
ValueCountFrequency (%)
e 350850
 
10.2%
a 284137
 
8.2%
o 281010
 
8.1%
t 271499
 
7.9%
n 246872
 
7.2%
i 241484
 
7.0%
r 217781
 
6.3%
s 210463
 
6.1%
l 157938
 
4.6%
d 133407
 
3.9%
Other values (49) 1055016
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4411456
> 99.9%
None 351
 
< 0.1%
Punctuation 238
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
706638
16.0%
e 350850
 
8.0%
a 284137
 
6.4%
o 281010
 
6.4%
t 271499
 
6.2%
n 246872
 
5.6%
i 241484
 
5.5%
r 217781
 
4.9%
s 210463
 
4.8%
l 157938
 
3.6%
Other values (85) 1442784
32.7%
None
ValueCountFrequency (%)
  177
50.4%
â 96
27.4%
½ 23
 
6.6%
 14
 
4.0%
¢ 11
 
3.1%
é 8
 
2.3%
ç 8
 
2.3%
± 3
 
0.9%
¼ 2
 
0.6%
ü 2
 
0.6%
Other values (5) 7
 
2.0%
Punctuation
ValueCountFrequency (%)
139
58.4%
32
 
13.4%
28
 
11.8%
16
 
6.7%
9
 
3.8%
9
 
3.8%
5
 
2.1%
Modifier Letters
ValueCountFrequency (%)
˜ 1
100.0%

homeType
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Single Family
9427 
Condo
 
333
Townhouse
 
113
Multiple Occupancy
 
60
Residential
 
27
Other values (5)
 
40

Length

Max length21
Median length13
Mean length12.71
Min length5

Characters and Unicode

Total characters127100
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle Family
2nd rowSingle Family
3rd rowSingle Family
4th rowSingle Family
5th rowSingle Family

Common Values

ValueCountFrequency (%)
Single Family 9427
94.3%
Condo 333
 
3.3%
Townhouse 113
 
1.1%
Multiple Occupancy 60
 
0.6%
Residential 27
 
0.3%
Apartment 19
 
0.2%
Mobile / Manufactured 10
 
0.1%
MultiFamily 5
 
0.1%
Vacant Land 4
 
< 0.1%
Other 2
 
< 0.1%

Length

2024-02-27T21:36:58.290642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-27T21:36:58.548599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
single 9427
48.3%
family 9427
48.3%
condo 333
 
1.7%
townhouse 113
 
0.6%
multiple 60
 
0.3%
occupancy 60
 
0.3%
residential 27
 
0.1%
apartment 19
 
0.1%
mobile 10
 
0.1%
10
 
0.1%
Other values (5) 25
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 19021
15.0%
i 18988
14.9%
n 9997
7.9%
e 9695
7.6%
a 9570
7.5%
9511
7.5%
y 9492
7.5%
m 9451
7.4%
F 9432
7.4%
S 9427
7.4%
Other values (22) 12516
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98073
77.2%
Uppercase Letter 19506
 
15.3%
Space Separator 9511
 
7.5%
Other Punctuation 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 19021
19.4%
i 18988
19.4%
n 9997
10.2%
e 9695
9.9%
a 9570
9.8%
y 9492
9.7%
m 9451
9.6%
g 9427
9.6%
o 902
 
0.9%
d 374
 
0.4%
Other values (10) 1156
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
F 9432
48.4%
S 9427
48.3%
C 333
 
1.7%
T 113
 
0.6%
M 85
 
0.4%
O 62
 
0.3%
R 27
 
0.1%
A 19
 
0.1%
V 4
 
< 0.1%
L 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9511
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 117579
92.5%
Common 9521
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 19021
16.2%
i 18988
16.1%
n 9997
8.5%
e 9695
8.2%
a 9570
8.1%
y 9492
8.1%
m 9451
8.0%
F 9432
8.0%
S 9427
8.0%
g 9427
8.0%
Other values (20) 3079
 
2.6%
Common
ValueCountFrequency (%)
9511
99.9%
/ 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 19021
15.0%
i 18988
14.9%
n 9997
7.9%
e 9695
7.6%
a 9570
7.5%
9511
7.5%
y 9492
7.5%
m 9451
7.4%
F 9432
7.4%
S 9427
7.4%
Other values (22) 12516
9.8%

latitude
Real number (ℝ)

Distinct9681
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.291191
Minimum30.08503
Maximum30.517323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:36:58.856811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum30.08503
5-th percentile30.156342
Q130.202516
median30.283664
Q330.366375
95-th percentile30.452515
Maximum30.517323
Range0.43229294
Interquartile range (IQR)0.16385889

Descriptive statistics

Standard deviation0.097075433
Coefficient of variation (CV)0.0032047414
Kurtosis-0.99447676
Mean30.291191
Median Absolute Deviation (MAD)0.081908226
Skewness0.29819182
Sum302911.91
Variance0.0094236398
MonotonicityNot monotonic
2024-02-27T21:36:59.116896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.36288643 3
 
< 0.1%
30.34660912 3
 
< 0.1%
30.19536972 3
 
< 0.1%
30.1853466 3
 
< 0.1%
30.19648743 3
 
< 0.1%
30.17970085 3
 
< 0.1%
30.18808174 3
 
< 0.1%
30.37572479 3
 
< 0.1%
30.18215561 3
 
< 0.1%
30.34636879 3
 
< 0.1%
Other values (9671) 9970
99.7%
ValueCountFrequency (%)
30.0850296 1
< 0.1%
30.08774757 1
< 0.1%
30.10301971 1
< 0.1%
30.10340118 1
< 0.1%
30.11837578 1
< 0.1%
30.12606812 1
< 0.1%
30.12716103 1
< 0.1%
30.12995529 1
< 0.1%
30.13022232 1
< 0.1%
30.13035393 1
< 0.1%
ValueCountFrequency (%)
30.51732254 1
< 0.1%
30.51691628 1
< 0.1%
30.51553535 1
< 0.1%
30.51532173 1
< 0.1%
30.51470566 1
< 0.1%
30.51423645 1
< 0.1%
30.51396561 1
< 0.1%
30.51317024 1
< 0.1%
30.51306152 1
< 0.1%
30.51288605 1
< 0.1%

longitude
Real number (ℝ)

Distinct8741
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-97.778476
Minimum-98.020477
Maximum-97.570633
Zeros0
Zeros (%)0.0%
Negative10000
Negative (%)100.0%
Memory size78.3 KiB
2024-02-27T21:36:59.389970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-98.020477
5-th percentile-97.913034
Q1-97.838594
median-97.76968
Q3-97.718313
95-th percentile-97.650397
Maximum-97.570633
Range0.44984436
Interquartile range (IQR)0.12028122

Descriptive statistics

Standard deviation0.084543413
Coefficient of variation (CV)-0.00086464237
Kurtosis-0.18989213
Mean-97.778476
Median Absolute Deviation (MAD)0.059303284
Skewness-0.30463036
Sum-977784.76
Variance0.0071475887
MonotonicityNot monotonic
2024-02-27T21:36:59.660584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-97.76957703 5
 
0.1%
-97.76528931 4
 
< 0.1%
-97.75722504 4
 
< 0.1%
-97.81352997 4
 
< 0.1%
-97.88847351 4
 
< 0.1%
-97.74790192 4
 
< 0.1%
-97.72940063 4
 
< 0.1%
-97.97138214 4
 
< 0.1%
-97.72867584 4
 
< 0.1%
-97.85913086 4
 
< 0.1%
Other values (8731) 9959
99.6%
ValueCountFrequency (%)
-98.02047729 1
< 0.1%
-98.01655579 1
< 0.1%
-98.01528168 1
< 0.1%
-98.01512909 1
< 0.1%
-98.01447296 1
< 0.1%
-98.01426697 1
< 0.1%
-98.01348877 1
< 0.1%
-98.01287079 1
< 0.1%
-98.0122757 1
< 0.1%
-98.01141357 1
< 0.1%
ValueCountFrequency (%)
-97.57063293 1
< 0.1%
-97.57133484 1
< 0.1%
-97.57167816 1
< 0.1%
-97.57196045 1
< 0.1%
-97.57234955 1
< 0.1%
-97.57244873 1
< 0.1%
-97.57250214 1
< 0.1%
-97.57323456 1
< 0.1%
-97.57331848 1
< 0.1%
-97.57436371 1
< 0.1%

garageSpaces
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2296
Minimum0
Maximum22
Zeros4458
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:36:59.943088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum22
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3281793
Coefficient of variation (CV)1.0801718
Kurtosis8.5330241
Mean1.2296
Median Absolute Deviation (MAD)1
Skewness1.4420673
Sum12296
Variance1.7640602
MonotonicityNot monotonic
2024-02-27T21:37:00.204409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 4458
44.6%
2 3665
36.6%
1 733
 
7.3%
3 631
 
6.3%
4 386
 
3.9%
6 55
 
0.5%
5 47
 
0.5%
8 9
 
0.1%
10 6
 
0.1%
7 5
 
0.1%
Other values (3) 5
 
0.1%
ValueCountFrequency (%)
0 4458
44.6%
1 733
 
7.3%
2 3665
36.6%
3 631
 
6.3%
4 386
 
3.9%
5 47
 
0.5%
6 55
 
0.5%
7 5
 
0.1%
8 9
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
12 2
 
< 0.1%
10 6
 
0.1%
9 2
 
< 0.1%
8 9
 
0.1%
7 5
 
0.1%
6 55
 
0.5%
5 47
 
0.5%
4 386
3.9%
3 631
6.3%

hasSpa
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
9175 
True
 
825
ValueCountFrequency (%)
False 9175
91.8%
True 825
 
8.2%
2024-02-27T21:37:00.414497image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

yearBuilt
Real number (ℝ)

Distinct113
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1988.5704
Minimum1905
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:37:00.672992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1905
5-th percentile1950
Q11975
median1993
Q32006
95-th percentile2017
Maximum2020
Range115
Interquartile range (IQR)31

Descriptive statistics

Standard deviation21.515272
Coefficient of variation (CV)0.010819467
Kurtosis-0.020228679
Mean1988.5704
Median Absolute Deviation (MAD)15
Skewness-0.69400517
Sum19885704
Variance462.90693
MonotonicityNot monotonic
2024-02-27T21:37:00.976619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 320
 
3.2%
1999 272
 
2.7%
2005 262
 
2.6%
2007 258
 
2.6%
1983 225
 
2.2%
1984 224
 
2.2%
2004 222
 
2.2%
1998 212
 
2.1%
2014 211
 
2.1%
2015 205
 
2.1%
Other values (103) 7589
75.9%
ValueCountFrequency (%)
1905 1
 
< 0.1%
1907 2
 
< 0.1%
1908 1
 
< 0.1%
1909 2
 
< 0.1%
1910 6
0.1%
1911 1
 
< 0.1%
1912 2
 
< 0.1%
1914 1
 
< 0.1%
1915 2
 
< 0.1%
1916 1
 
< 0.1%
ValueCountFrequency (%)
2020 88
0.9%
2019 145
1.5%
2018 163
1.6%
2017 164
1.6%
2016 189
1.9%
2015 205
2.1%
2014 211
2.1%
2013 197
2.0%
2012 142
1.4%
2011 107
1.1%

numOfPatioAndPorchFeatures
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6672
Minimum0
Maximum8
Zeros6072
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:37:01.220259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.98637795
Coefficient of variation (CV)1.4783842
Kurtosis2.0384828
Mean0.6672
Median Absolute Deviation (MAD)0
Skewness1.5094298
Sum6672
Variance0.97294145
MonotonicityNot monotonic
2024-02-27T21:37:01.453075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 6072
60.7%
1 1977
 
19.8%
2 1331
 
13.3%
3 479
 
4.8%
4 117
 
1.2%
5 19
 
0.2%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 6072
60.7%
1 1977
 
19.8%
2 1331
 
13.3%
3 479
 
4.8%
4 117
 
1.2%
5 19
 
0.2%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 3
 
< 0.1%
5 19
 
0.2%
4 117
 
1.2%
3 479
 
4.8%
2 1331
 
13.3%
1 1977
 
19.8%
0 6072
60.7%

lotSizeSqFt
Real number (ℝ)

SKEWED 

Distinct1105
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20710.284
Minimum100
Maximum34154525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:37:01.738147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4228.8
Q16534
median8189
Q310890
95-th percentile25700.4
Maximum34154525
Range34154425
Interquartile range (IQR)4356

Descriptive statistics

Standard deviation448833.82
Coefficient of variation (CV)21.672026
Kurtosis4518.4574
Mean20710.284
Median Absolute Deviation (MAD)2048
Skewness64.996148
Sum2.0710284 × 108
Variance2.0145179 × 1011
MonotonicityNot monotonic
2024-02-27T21:37:02.040438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12196.8 196
 
2.0%
11761.2 192
 
1.9%
11325.6 190
 
1.9%
6969 162
 
1.6%
12632.4 159
 
1.6%
7840 158
 
1.6%
7405 158
 
1.6%
6098 154
 
1.5%
8712 148
 
1.5%
10890 148
 
1.5%
Other values (1095) 8335
83.4%
ValueCountFrequency (%)
100 2
< 0.1%
113 1
< 0.1%
117 1
< 0.1%
217 1
< 0.1%
392 2
< 0.1%
396 1
< 0.1%
400 1
< 0.1%
435 1
< 0.1%
522 1
< 0.1%
609 2
< 0.1%
ValueCountFrequency (%)
34154524.8 1
< 0.1%
26179560 1
< 0.1%
8581320 1
< 0.1%
5967720 1
< 0.1%
5880600 1
< 0.1%
2988216 1
< 0.1%
2178000 1
< 0.1%
1516323.6 1
< 0.1%
1003622.4 1
< 0.1%
871200 1
< 0.1%

avgSchoolRating
Real number (ℝ)

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7662361
Minimum2.3333333
Maximum9.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:37:02.659803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2.3333333
5-th percentile3
Q14
median5.6666667
Q37
95-th percentile9
Maximum9.5
Range7.1666667
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8619399
Coefficient of variation (CV)0.32290387
Kurtosis-1.1868835
Mean5.7662361
Median Absolute Deviation (MAD)1.6666667
Skewness0.08933698
Sum57662.361
Variance3.4668203
MonotonicityNot monotonic
2024-02-27T21:37:02.906185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7 1268
12.7%
6.666666667 821
 
8.2%
3.333333333 820
 
8.2%
4 760
 
7.6%
3.666666667 632
 
6.3%
6 590
 
5.9%
5 532
 
5.3%
4.333333333 504
 
5.0%
8 474
 
4.7%
5.333333333 460
 
4.6%
Other values (17) 3139
31.4%
ValueCountFrequency (%)
2.333333333 47
 
0.5%
2.666666667 144
 
1.4%
3 410
4.1%
3.333333333 820
8.2%
3.5 2
 
< 0.1%
3.666666667 632
6.3%
4 760
7.6%
4.333333333 504
5.0%
4.666666667 338
3.4%
5 532
5.3%
ValueCountFrequency (%)
9.5 14
 
0.1%
9.333333333 46
 
0.5%
9 443
 
4.4%
8.666666667 358
 
3.6%
8.5 5
 
0.1%
8.333333333 445
 
4.5%
8 474
 
4.7%
7.666666667 164
 
1.6%
7.333333333 297
 
3.0%
7 1268
12.7%

MedianStudentsPerTeacher
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.8577
Minimum10
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:37:03.127476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q114
median15
Q316
95-th percentile17
Maximum19
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7484726
Coefficient of variation (CV)0.11768124
Kurtosis-0.46199531
Mean14.8577
Median Absolute Deviation (MAD)1
Skewness-0.34519742
Sum148577
Variance3.0571564
MonotonicityNot monotonic
2024-02-27T21:37:03.336740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
16 2563
25.6%
14 2139
21.4%
15 1487
14.9%
17 1236
12.4%
13 1099
11.0%
12 556
 
5.6%
18 460
 
4.6%
11 431
 
4.3%
10 25
 
0.2%
19 4
 
< 0.1%
ValueCountFrequency (%)
10 25
 
0.2%
11 431
 
4.3%
12 556
 
5.6%
13 1099
11.0%
14 2139
21.4%
15 1487
14.9%
16 2563
25.6%
17 1236
12.4%
18 460
 
4.6%
19 4
 
< 0.1%
ValueCountFrequency (%)
19 4
 
< 0.1%
18 460
 
4.6%
17 1236
12.4%
16 2563
25.6%
15 1487
14.9%
14 2139
21.4%
13 1099
11.0%
12 556
 
5.6%
11 431
 
4.3%
10 25
 
0.2%

numOfBathrooms
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6921
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:37:03.554655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97920553
Coefficient of variation (CV)0.36373297
Kurtosis2.6524767
Mean2.6921
Median Absolute Deviation (MAD)1
Skewness1.038979
Sum26921
Variance0.95884347
MonotonicityNot monotonic
2024-02-27T21:37:03.757835image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 4030
40.3%
3 3558
35.6%
4 1270
 
12.7%
1 622
 
6.2%
5 253
 
2.5%
6 88
 
0.9%
2.5 86
 
0.9%
7 35
 
0.4%
3.5 29
 
0.3%
1.5 14
 
0.1%
Other values (5) 15
 
0.1%
ValueCountFrequency (%)
1 622
 
6.2%
1.5 14
 
0.1%
2 4030
40.3%
2.5 86
 
0.9%
3 3558
35.6%
3.5 29
 
0.3%
4 1270
 
12.7%
4.5 3
 
< 0.1%
5 253
 
2.5%
5.5 1
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
8 8
 
0.1%
7 35
 
0.4%
6.5 1
 
< 0.1%
6 88
 
0.9%
5.5 1
 
< 0.1%
5 253
 
2.5%
4.5 3
 
< 0.1%
4 1270
12.7%
3.5 29
 
0.3%

numOfBedrooms
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4492
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-02-27T21:37:03.972799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81344055
Coefficient of variation (CV)0.23583456
Kurtosis2.0665322
Mean3.4492
Median Absolute Deviation (MAD)1
Skewness0.5392538
Sum34492
Variance0.66168553
MonotonicityNot monotonic
2024-02-27T21:37:04.151319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 4919
49.2%
4 3453
34.5%
5 787
 
7.9%
2 688
 
6.9%
1 69
 
0.7%
6 62
 
0.6%
8 14
 
0.1%
7 7
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
1 69
 
0.7%
2 688
 
6.9%
3 4919
49.2%
4 3453
34.5%
5 787
 
7.9%
6 62
 
0.6%
7 7
 
0.1%
8 14
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 14
 
0.1%
7 7
 
0.1%
6 62
 
0.6%
5 787
 
7.9%
4 3453
34.5%
3 4919
49.2%
2 688
 
6.9%
1 69
 
0.7%

priceRange
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
250000-350000
2356 
350000-450000
2301 
450000-650000
2275 
650000+
1819 
0-250000
1249 

Length

Max length13
Median length13
Mean length11.2841
Min length7

Characters and Unicode

Total characters112841
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row650000+
2nd row350000-450000
3rd row0-250000
4th row0-250000
5th row650000+

Common Values

ValueCountFrequency (%)
250000-350000 2356
23.6%
350000-450000 2301
23.0%
450000-650000 2275
22.8%
650000+ 1819
18.2%
0-250000 1249
12.5%

Length

2024-02-27T21:37:04.391150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-27T21:37:04.633239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
250000-350000 2356
23.6%
350000-450000 2301
23.0%
450000-650000 2275
22.8%
650000 1819
18.2%
0-250000 1249
12.5%

Most occurring characters

ValueCountFrequency (%)
0 68977
61.1%
5 16932
 
15.0%
- 8181
 
7.3%
3 4657
 
4.1%
4 4576
 
4.1%
6 4094
 
3.6%
2 3605
 
3.2%
+ 1819
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102841
91.1%
Dash Punctuation 8181
 
7.3%
Math Symbol 1819
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68977
67.1%
5 16932
 
16.5%
3 4657
 
4.5%
4 4576
 
4.4%
6 4094
 
4.0%
2 3605
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 8181
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1819
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112841
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68977
61.1%
5 16932
 
15.0%
- 8181
 
7.3%
3 4657
 
4.1%
4 4576
 
4.1%
6 4094
 
3.6%
2 3605
 
3.2%
+ 1819
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112841
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68977
61.1%
5 16932
 
15.0%
- 8181
 
7.3%
3 4657
 
4.1%
4 4576
 
4.1%
6 4094
 
3.6%
2 3605
 
3.2%
+ 1819
 
1.6%

Interactions

2024-02-27T21:36:52.722678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:28.453443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:31.558197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:33.714470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:36.164308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:38.602151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:41.116108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:43.515958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:45.803581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:48.050864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:50.564921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:52.930801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:28.748518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:31.789829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:33.949693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:36.400666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:38.861861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:41.330831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:43.730677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:46.025206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:48.225320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:50.743839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:53.096358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:28.924773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:31.966006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:34.164861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:36.635326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:39.053693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:41.537792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:43.914026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:46.228083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:48.418026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:50.969753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:53.287029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:29.120327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:32.141921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:34.359676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:36.836461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:39.258512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:41.751827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:44.109598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:46.441920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:48.621738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:51.143596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:53.473731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:29.956514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:32.319690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:34.552670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:37.021699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:39.447909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:41.951679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:44.282963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:46.619829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:49.058677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:51.326000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:53.657016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:30.180068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:32.519472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:34.827922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:37.221584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:39.661205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:42.138711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:44.519959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:46.826015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:49.244812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:51.511241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:53.859703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:30.433783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:32.712948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:35.043620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:37.431527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:39.892761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:42.458001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:44.753055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:47.055182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:49.458058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:51.778824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:54.073748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:30.665198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:32.931473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:35.292074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:37.633552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:40.191530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:42.697609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:44.975404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:47.304308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:49.674967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:51.965623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:54.260026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:30.903901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:33.120754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:35.521971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:37.812608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:40.430028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:42.914543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:45.214311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:47.507517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:49.892631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:52.161106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:54.452869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:31.116316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:33.320683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:35.721775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:37.995989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:40.659858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:43.116950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:45.418070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:47.669835image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:50.216154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:52.343101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:54.628648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:31.330838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:33.528562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:35.973483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:38.173917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:40.905079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:43.319780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:45.613519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:47.866074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:50.391721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-27T21:36:52.525855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-27T21:36:54.919859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-27T21:36:55.415338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

uidcitydescriptionhomeTypelatitudelongitudegarageSpaceshasSpayearBuiltnumOfPatioAndPorchFeatureslotSizeSqFtavgSchoolRatingMedianStudentsPerTeachernumOfBathroomsnumOfBedroomspriceRange
01748austinMULTIPLE OFFERS submit best & final to Agent by Mon 21st - 5pm. Appt with Agent. RARE PANORAMIC VIEW LOT IN JESTER ESTATES SEE FOR MILES!! Home sits on Cul-de-sac & backs to a Preserve. Stunning remodeled Kitchen & Bathrooms. Master suite is a private sanctuary with chic master bath, huge bedroom, walk-in closet & private deck. Jester has a pool, park, tennis courts & feeds into Anderson High. This home has been well loved & features 3 living areas, an office, & 3 car garage.Single Family30.380089-97.8006210False19880102366.07.000000174.04650000+
113380austin4644 Hoffman Dr, Austin, TX 78749 is a single family home that contains 2,059 sq ft and was built in 1997. It contains 4 bedrooms and 3 bathrooms. \r\n \r\nSingle Family30.199486-97.8599470False199706534.06.666667163.04350000-450000
24115austin6804 Canal St, Austin, TX 78741 is a single family home that contains 832 sq ft and was built in 1952. It contains 2 bedrooms and 1 bathroom. \r\n \r\nSingle Family30.227398-97.6960830False195205619.03.333333111.020-250000
36926austinBeautiful large lot with established trees. Lovely, light filled, spacious home, with large dining area, living room and kitchen in open floor plan layout. Large family-friendly back yard, newly redone patio that spans the back of the home with built-in fire pit in the yard and a newly built large shed. New laminate floors and tile throughout, renovated bathroom in 2017. Upgraded kitchen with stainless appliances, new washer/dryer. Built-in closet, laundry room and utility room. LED recessed lighting, low-profile ceiling fans throughout. Wired for Google Fiber.\r\n\r\nNeighborhood Description\r\n\r\nThe Garrison Park Neighborhood Planning Area is located in southwest Austin. The boundaries for the planning area are Stassney on the north, the City of Sunset Valley on the west, William Cannon on the south and South First on the east.Single Family30.205469-97.7923514False197606416.04.000000142.040-250000
414480austinStunning NW Hills designer remodel by Cedar and Oak Homes ~ Wonderful open & bright concept w/vaulted ceilings & formal dining room ~ Gorgeous kitchen features custom wood cabinetry, quartz countertops, double pantry, custom built-in breakfast nook and KitchenAid appliances ~ Beautiful oak engineered hardwood flooring throughout ~ Spacious Master bedroom with fantastic master bathroom ensuite ~ All new exterior Hardie Plank siding, Elevate Low-E windows, mechanical systems & fantastic new landscapingSingle Family30.345106-97.7674262False1984010759.07.000000163.05650000+
513448austin1808 Saint Albans Blvd, Austin, TX 78745 is a single family home that contains 1,554 sq ft and was built in 1964. It contains 3 bedrooms and 2 bathrooms. \r\n \r\nSingle Family30.218163-97.7942500False196409452.04.666667142.03450000-650000
61996austinStunning 1story contemporary home on a beautifully landscaped private Corner/cul-de-sac/1acre lot. Open entertaining floorplan~an attached casita w/ Fleetwood doors that disappear into the walls for gracious indoor/outdoor entertaining~pool and spa invite you w/ beautiful hill country views~& much more. The home features 4/5bed depending how you use the office, 4 full baths, multiple living areas, climate controlled wine room, spa like master bath & 3car garage. This home has extensive upgrades. MUST SEE!Single Family30.283981-97.8857800False2015045302.45.666667164.05650000+
711353austinHIGH RENTABLE AREA! Beautifully kept duplex located minutes away from great restaurants, shopping, and easy access to I-35 and HWY 183! Each unit features 3 bed 2 bath with an amazing floor plan!Multiple Occupancy30.327396-97.6939011False196708886.03.333333154.060-250000
84643austinBeautifully, south Austin home on large corner lot adorned with wonderful Texas live oak trees! This 2-store home boasts a spacious kitchen, family room, formal living room with vaulted ceilings, and dining area. All bedrooms are centrally located on the spacious 2nd floor along with a multi-use loft space. Located moments from Mopac and local shopping. *Please Text/Call Agent to schedule showing a minimum of 2 hours prior to showing, owner works from home* *Showings to begin Monday 5/14/18*Single Family30.209915-97.8452762False198307535.04.666667143.03250000-350000
93118austinThis fantastic property is walking distance to Walnut Creek! Spacious floor plan with beautiful wood laminate throughout living. Cozy fireplace that makes it perfect for entertainment. HIGH ceilings! Kitchen features granite countertops and stainless steel appliances! Ample cabinet space! Great size master with updated bath and double vanity! 4th bedroom could be used for a play space or office. The property is complete with an amazing backyard space with extended decking! Will not last long!Single Family30.381290-97.6676250False1972013068.05.000000152.04250000-350000
uidcitydescriptionhomeTypelatitudelongitudegarageSpaceshasSpayearBuiltnumOfPatioAndPorchFeatureslotSizeSqFtavgSchoolRatingMedianStudentsPerTeachernumOfBathroomsnumOfBedroomspriceRange
99901554austinSpectacular stone & stucco home on a rare one acre cul-de-sac lot! Backs to a remarkable greenbelt w/panoramic views of hill country! Wonderful open floor plan w/tons of upgrades & custom details throughout- beamed ceilings, canterra doors, granite counter tops, mudroom, huge bonus room over garage & outdoor shower. Every room imaginable- a cozy family rm w/fireplace, game rm, office & media theater. Great outdoor living & rare 3-car garage. In wonderful gated community w/lots of great amenities to offer!Single Family30.349260-97.9153520False2005042253.25.778694155.55650000+
99915450austinSituated on an oversized lot and backing to Little Bouldin Creek, this charming property has truly endless possibilities. The home has a 2,148 SQFT footprint (per TCAD) built in 1964 with a single owner. The property boasts a double driveway capable of becoming a governor's drive, lush landscaping, mature and fruit bearing trees, and a private grotto to enjoy the tranquil sound of the spring-fed creek. This 4 bedroom, 2 bath home with a converted single car garage is perfect for you to make your own or start anew. Survey + Floorplan available - Elevation Certificate showing home is not located within floodplain is available with acceptable offer.\r\nAbout the Neighborhood: Dawson is a South Austin neighborhood that in modernizing but retains much of its vintage atmosphere. Close to the crowds and nightlife of the SoCo area, but far enough away to be a quiet Neighborhood. Minutes from St. Edwards University, hospital, I35 + more. This is your chance for an amazing opportunity in 78704!Single Family30.230824-97.7621842False1964012196.83.666667122.04450000-650000
99921386austin131 Blazing Star Dr, Austin, TX 78737 is a single family home that contains 4,387 sq ft and was built in 2006. It contains 4 bedrooms and 4 bathrooms. \r\n \r\nSingle Family30.170786-98.0045933False2006016117.28.000000154.04450000-650000
99934366austin7016 Boyle Dr, Austin, TX 78724 is a single family home that contains 2,199 sq ft and was built in 2016. It contains 3 bedrooms and 3 bathrooms. \r\n \r\nSingle Family30.305607-97.6458360False201605401.03.666667143.03350000-450000
99942147austinGorgeous One story well maintained (3 year old), Open floor plan, 4 Bedroom Home with Abundance of Natural Light!!! Beautiful Kitchen with plenty of Counter & Cabinets space, Center Island & Opens to the Family and Dining Room. Covered Patio. Close to major highways and Shopping. WELCOME HOME!!!Single Family30.131514-97.7749252False201617492.05.333333152.04250000-350000
99958614austinUpdated Great Hills Opportunity! Large corner lot with mature live oaks. Backyard oasis is an entertainer's dream with a hill country feel, featuring an inground pool and expansive upper & lower deck! Interior features large family room overlooking backyard. Kitchen includes center island & updated appliances with direct access to lower deck. Master suite includes separate office, two walk-in closets with top deck access. Two large bedrooms downstairs. Plenty of storage space. Anderson High School. Hurry!Single Family30.409927-97.7633902False1982315246.07.000000173.03650000+
999610505austinAdorable 3/2 in the heart of South Austin! Situated on a large corner lot with gorgeous trees. The beautiful outbuilding could be an artist's retreat or home office! Original owner and the pride of ownership really shows. Very well maintained home in one of S Austin's most convenient locations to downtown! Easy access to I-35 and area amenities and schools. A true South Austin gem!Single Family30.206074-97.7794720False197006577.04.000000142.03250000-350000
99976942austin7322 Gaines Mill Ln, Austin, TX 78745 is a single family home that contains 1,779 sq ft and was built in 1981. It contains 3 bedrooms and 2 bathrooms. \r\n \r\nSingle Family30.198936-97.8109130False198117623.04.000000132.03350000-450000
99981845austin5213 Doe Valley Ln, Austin, TX 78759 is a single family home that contains 2,018 sq ft and was built in 1979. It contains 3 bedrooms and 3 bathrooms. \r\n \r\nSingle Family30.391880-97.7612920False1979213939.27.000000173.03250000-350000
99994425austinBeautiful single-story home with open floor plan in great neighborhood. Spacious living room opens to dining area and kitchen with breakfast bar, granite counters, 42 inch cabinets, and tiled backsplash. Wood laminate flooring in main living areas. 9 ft ceilings throughout. Master suite with dual vanity and garden tub. Master planned community with on-site elementary school and lots of amenities. Great location close to major tech employers w/easy access to Hwy 290 and just 15 min from downtown Austin.Single Family30.346609-97.6149831False201304878.05.000000112.030-250000